{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Python for Algorithmic Trading "]}, {"cell_type": "markdown", "metadata": {}, "source": ["**Chapter 09 &mdash; FX Trading with FXCM**"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Risk Disclaimer"]}, {"cell_type": "markdown", "metadata": {}, "source": ["<font size=\"-1\">\n", "Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits. Leverage can work against you. Due to the certain restrictions imposed by the local law and regulation, German resident retail client(s) could sustain a total loss of deposited funds but are not subject to subsequent payment obligations beyond the deposited funds. Be aware and fully understand all risks associated with the market and trading. Prior to trading any products, carefully consider your financial situation and experience level. Any opinions, news, research, analyses, prices, or other information is provided as general market commentary, and does not constitute investment advice. FXCM & TPQ will not accept liability for any loss or damage, including without limitation to, any loss of profit, which may arise directly or indirectly from use of or reliance on such information.\n", "</font>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Author Disclaimer"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The author is neither an employee, agent nor representative of FXCM and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of FXCM or any form of personal or investment advice. FXCM assumes no responsibility for any loss or damage, including but not limited to, any loss or gain arising out of the direct or indirect use of this or any other content. Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits."]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Retrieving Tick Data"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import time\n", "import numpy as np\n", "import pandas as pd\n", "import datetime as dt\n", "from pylab import mpl, plt"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["plt.style.use('seaborn')\n", "mpl.rcParams['font.family'] = 'serif'\n", "%matplotlib inline"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["from fxcmpy import fxcmpy_tick_data_reader as tdr"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"]}], "source": ["print(tdr.get_available_symbols())"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": ["start = dt.datetime(2020, 3, 25)\n", "stop = dt.datetime(2020, 3, 30)"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["td = tdr('EURUSD', start, stop)"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "Index: 4504288 entries, 03/22/2020 21:12:02.256 to 03/27/2020 20:59:00.022\n", "Data columns (total 2 columns):\n", " #   Column  Dtype  \n", "---  ------  -----  \n", " 0   Bid     float64\n", " 1   Ask     float64\n", "dtypes: float64(2)\n", "memory usage: 103.1+ MB\n"]}], "source": ["td.get_raw_data().info()"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 4504288 entries, 2020-03-22 21:12:02.256000 to 2020-03-27 20:59:00.022000\n", "Data columns (total 2 columns):\n", " #   Column  Dtype  \n", "---  ------  -----  \n", " 0   Bid     float64\n", " 1   Ask     float64\n", "dtypes: float64(2)\n", "memory usage: 103.1 MB\n"]}], "source": ["td.get_data().info()"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Bid</th>\n", "      <th>Ask</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-03-22 21:12:02.256</th>\n", "      <td>1.07006</td>\n", "      <td>1.07050</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-22 21:12:02.258</th>\n", "      <td>1.07002</td>\n", "      <td>1.07050</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-22 21:12:02.259</th>\n", "      <td>1.07003</td>\n", "      <td>1.07033</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-22 21:12:02.653</th>\n", "      <td>1.07003</td>\n", "      <td>1.07034</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-22 21:12:02.749</th>\n", "      <td>1.07000</td>\n", "      <td>1.07034</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                             Bid      Ask\n", "2020-03-22 21:12:02.256  1.07006  1.07050\n", "2020-03-22 21:12:02.258  1.07002  1.07050\n", "2020-03-22 21:12:02.259  1.07003  1.07033\n", "2020-03-22 21:12:02.653  1.07003  1.07034\n", "2020-03-22 21:12:02.749  1.07000  1.07034"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["td.get_data().head()"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": ["sub = td.get_data(start='2020-03-25 12:00:00',\n", "                  end='2020-03-25 12:15:00')"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Bid</th>\n", "      <th>Ask</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-03-25 12:00:00.067</th>\n", "      <td>1.08109</td>\n", "      <td>1.0811</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-25 12:00:00.072</th>\n", "      <td>1.08110</td>\n", "      <td>1.0811</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-25 12:00:00.074</th>\n", "      <td>1.08109</td>\n", "      <td>1.0811</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-25 12:00:00.078</th>\n", "      <td>1.08111</td>\n", "      <td>1.0811</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-03-25 12:00:00.121</th>\n", "      <td>1.08112</td>\n", "      <td>1.0811</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                             Bid     Ask\n", "2020-03-25 12:00:00.067  1.08109  1.0811\n", "2020-03-25 12:00:00.072  1.08110  1.0811\n", "2020-03-25 12:00:00.074  1.08109  1.0811\n", "2020-03-25 12:00:00.078  1.08111  1.0811\n", "2020-03-25 12:00:00.121  1.08112  1.0811"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["sub.head()"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": ["sub['Mid'] = sub.mean(axis=1)"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": ["sub['SMA'] = sub['Mid'].rolling(1000).mean()"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["sub[['Mid', 'SMA']].plot(figsize=(10, 6), lw=1.5);\n", "plt.savefig('../../images/ch09/fxcm_plot_01.png')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Retrieving Candles Data"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": ["from fxcmpy import fxcmpy_candles_data_reader as cdr"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"]}], "source": ["print(cdr.get_available_symbols())"]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": ["start = dt.datetime(2020, 4, 1)\n", "stop = dt.datetime(2020, 5, 1)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`period` must be one of `m1`, `H1` or `D1`"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": ["period = 'H1'"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": ["candles = cdr('EURUSD', start, stop, period)"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": ["data = candles.get_data()"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 600 entries, 2020-03-29 21:00:00 to 2020-05-01 20:00:00\n", "Data columns (total 8 columns):\n", " #   Column    Non-Null Count  Dtype  \n", "---  ------    --------------  -----  \n", " 0   BidOpen   600 non-null    float64\n", " 1   BidHigh   600 non-null    float64\n", " 2   BidLow    600 non-null    float64\n", " 3   BidClose  600 non-null    float64\n", " 4   AskOpen   600 non-null    float64\n", " 5   AskHigh   600 non-null    float64\n", " 6   AskLow    600 non-null    float64\n", " 7   AskClose  600 non-null    float64\n", "dtypes: float64(8)\n", "memory usage: 42.2 KB\n"]}], "source": ["data.info()"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>BidOpen</th>\n", "      <th>BidHigh</th>\n", "      <th>BidLow</th>\n", "      <th>BidClose</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-05-01 16:00:00</th>\n", "      <td>1.09976</td>\n", "      <td>1.09996</td>\n", "      <td>1.09850</td>\n", "      <td>1.09874</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 17:00:00</th>\n", "      <td>1.09874</td>\n", "      <td>1.09888</td>\n", "      <td>1.09785</td>\n", "      <td>1.09818</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 18:00:00</th>\n", "      <td>1.09818</td>\n", "      <td>1.09820</td>\n", "      <td>1.09757</td>\n", "      <td>1.09766</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 19:00:00</th>\n", "      <td>1.09766</td>\n", "      <td>1.09816</td>\n", "      <td>1.09747</td>\n", "      <td>1.09793</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 20:00:00</th>\n", "      <td>1.09793</td>\n", "      <td>1.09812</td>\n", "      <td>1.09730</td>\n", "      <td>1.09788</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     BidOpen  BidHigh   BidLow  BidClose\n", "2020-05-01 16:00:00  1.09976  1.09996  1.09850   1.09874\n", "2020-05-01 17:00:00  1.09874  1.09888  1.09785   1.09818\n", "2020-05-01 18:00:00  1.09818  1.09820  1.09757   1.09766\n", "2020-05-01 19:00:00  1.09766  1.09816  1.09747   1.09793\n", "2020-05-01 20:00:00  1.09793  1.09812  1.09730   1.09788"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["data[data.columns[:4]].tail()"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>AskOpen</th>\n", "      <th>AskHigh</th>\n", "      <th>AskLow</th>\n", "      <th>AskClose</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-05-01 16:00:00</th>\n", "      <td>1.09980</td>\n", "      <td>1.09998</td>\n", "      <td>1.09853</td>\n", "      <td>1.09876</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 17:00:00</th>\n", "      <td>1.09876</td>\n", "      <td>1.09891</td>\n", "      <td>1.09786</td>\n", "      <td>1.09818</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 18:00:00</th>\n", "      <td>1.09818</td>\n", "      <td>1.09822</td>\n", "      <td>1.09758</td>\n", "      <td>1.09768</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 19:00:00</th>\n", "      <td>1.09768</td>\n", "      <td>1.09818</td>\n", "      <td>1.09748</td>\n", "      <td>1.09795</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-01 20:00:00</th>\n", "      <td>1.09795</td>\n", "      <td>1.09856</td>\n", "      <td>1.09733</td>\n", "      <td>1.09841</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     AskOpen  AskHigh   AskLow  AskClose\n", "2020-05-01 16:00:00  1.09980  1.09998  1.09853   1.09876\n", "2020-05-01 17:00:00  1.09876  1.09891  1.09786   1.09818\n", "2020-05-01 18:00:00  1.09818  1.09822  1.09758   1.09768\n", "2020-05-01 19:00:00  1.09768  1.09818  1.09748   1.09795\n", "2020-05-01 20:00:00  1.09795  1.09856  1.09733   1.09841"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["data[data.columns[4:]].tail()"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": ["data['MidClose'] = data[['BidClose', 'AskClose']].mean(axis=1)"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": ["data['SMA1'] = data['MidClose'].rolling(30).mean()\n", "data['SMA2'] = data['MidClose'].rolling(100).mean()"]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["data[['MidClose', 'SMA1', 'SMA2']].plot(figsize=(10, 6));\n", "plt.savefig('../../images/ch09/fxcm_plot_02.png')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Connecting to the API"]}, {"cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": ["import fxcmpy"]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [{"data": {"text/plain": ["'1.2.6'"]}, "execution_count": 28, "metadata": {}, "output_type": "execute_result"}], "source": ["fxcmpy.__version__"]}, {"cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": ["api = fxcmpy.fxcmpy(config_file='../pyalgo.cfg')"]}, {"cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": ["instruments = api.get_instruments()"]}, {"cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF', 'EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD', 'EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'CHF/JPY', 'GBP/CHF', 'EUR/AUD', 'EUR/CAD', 'AUD/CAD', 'AUD/JPY', 'CAD/JPY', 'NZD/JPY', 'GBP/CAD', 'GBP/NZD', 'GBP/AUD', 'AUD/NZD', 'USD/SEK', 'EUR/SEK', 'EUR/NOK', 'USD/NOK', 'USD/MXN', 'AUD/CHF', 'EUR/NZD', 'USD/ZAR', 'USD/HKD', 'ZAR/JPY', 'USD/TRY', 'EUR/TRY', 'NZD/CHF', 'CAD/CHF', 'NZD/CAD', 'TRY/JPY', 'USD/ILS', 'USD/CNH', 'AUS200', 'ESP35', 'FRA40', 'GER30', 'HKG33', 'JPN225', 'NAS100', 'SPX500', 'UK100', 'US30', 'Copper', 'CHN50', 'EUSTX50', 'USDOLLAR', 'US2000', 'USOil', 'UKOil', 'SOYF', 'NGAS', 'WHEATF', 'CORNF', 'Bund', 'XAU/USD', 'XAG/USD', 'EMBasket', 'JPYBasket', 'BTC/USD', 'BCH/USD', 'ETH/USD', 'LTC/USD', 'XRP/USD', 'CryptoMajor', 'EOS/USD', 'XLM/USD', 'USEquities']\n"]}], "source": ["print(instruments)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Retrieving Historical Data"]}, {"cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": ["candles = api.get_candles('USD/JPY', period='D1', number=10)"]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>bidopen</th>\n", "      <th>bidclose</th>\n", "      <th>bidhigh</th>\n", "      <th>bidlow</th>\n", "    </tr>\n", "    <tr>\n", "      <th>date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-05-21 21:00:00</th>\n", "      <td>107.502</td>\n", "      <td>107.598</td>\n", "      <td>107.843</td>\n", "      <td>107.503</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-22 21:00:00</th>\n", "      <td>107.598</td>\n", "      <td>107.595</td>\n", "      <td>107.757</td>\n", "      <td>107.316</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-24 21:00:00</th>\n", "      <td>107.595</td>\n", "      <td>107.593</td>\n", "      <td>107.593</td>\n", "      <td>107.545</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-25 21:00:00</th>\n", "      <td>107.593</td>\n", "      <td>107.667</td>\n", "      <td>107.776</td>\n", "      <td>107.545</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-26 21:00:00</th>\n", "      <td>107.667</td>\n", "      <td>107.501</td>\n", "      <td>107.917</td>\n", "      <td>107.395</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-27 21:00:00</th>\n", "      <td>107.501</td>\n", "      <td>107.706</td>\n", "      <td>107.941</td>\n", "      <td>107.359</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-28 21:00:00</th>\n", "      <td>107.706</td>\n", "      <td>107.591</td>\n", "      <td>107.897</td>\n", "      <td>107.564</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-29 21:00:00</th>\n", "      <td>107.591</td>\n", "      <td>107.767</td>\n", "      <td>107.891</td>\n", "      <td>107.071</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-31 21:00:00</th>\n", "      <td>107.628</td>\n", "      <td>107.656</td>\n", "      <td>107.682</td>\n", "      <td>107.628</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-06-01 21:00:00</th>\n", "      <td>107.656</td>\n", "      <td>107.682</td>\n", "      <td>107.850</td>\n", "      <td>107.373</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     bidopen  bidclose  bidhigh   bidlow\n", "date                                                    \n", "2020-05-21 21:00:00  107.502   107.598  107.843  107.503\n", "2020-05-22 21:00:00  107.598   107.595  107.757  107.316\n", "2020-05-24 21:00:00  107.595   107.593  107.593  107.545\n", "2020-05-25 21:00:00  107.593   107.667  107.776  107.545\n", "2020-05-26 21:00:00  107.667   107.501  107.917  107.395\n", "2020-05-27 21:00:00  107.501   107.706  107.941  107.359\n", "2020-05-28 21:00:00  107.706   107.591  107.897  107.564\n", "2020-05-29 21:00:00  107.591   107.767  107.891  107.071\n", "2020-05-31 21:00:00  107.628   107.656  107.682  107.628\n", "2020-06-01 21:00:00  107.656   107.682  107.850  107.373"]}, "execution_count": 33, "metadata": {}, "output_type": "execute_result"}], "source": ["candles[candles.columns[:4]]"]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>askopen</th>\n", "      <th>askclose</th>\n", "      <th>askhigh</th>\n", "      <th>asklow</th>\n", "      <th>tickqty</th>\n", "    </tr>\n", "    <tr>\n", "      <th>date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-05-21 21:00:00</th>\n", "      <td>107.589</td>\n", "      <td>107.613</td>\n", "      <td>107.854</td>\n", "      <td>107.528</td>\n", "      <td>189628</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-22 21:00:00</th>\n", "      <td>107.613</td>\n", "      <td>107.661</td>\n", "      <td>107.770</td>\n", "      <td>107.329</td>\n", "      <td>168381</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-24 21:00:00</th>\n", "      <td>107.661</td>\n", "      <td>107.660</td>\n", "      <td>107.667</td>\n", "      <td>107.635</td>\n", "      <td>6</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-25 21:00:00</th>\n", "      <td>107.660</td>\n", "      <td>107.753</td>\n", "      <td>107.789</td>\n", "      <td>107.576</td>\n", "      <td>112616</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-26 21:00:00</th>\n", "      <td>107.753</td>\n", "      <td>107.566</td>\n", "      <td>107.930</td>\n", "      <td>107.407</td>\n", "      <td>191402</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-27 21:00:00</th>\n", "      <td>107.566</td>\n", "      <td>107.740</td>\n", "      <td>107.955</td>\n", "      <td>107.373</td>\n", "      <td>274980</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-28 21:00:00</th>\n", "      <td>107.740</td>\n", "      <td>107.676</td>\n", "      <td>107.909</td>\n", "      <td>107.576</td>\n", "      <td>251491</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-29 21:00:00</th>\n", "      <td>107.676</td>\n", "      <td>107.843</td>\n", "      <td>107.903</td>\n", "      <td>107.086</td>\n", "      <td>344564</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-31 21:00:00</th>\n", "      <td>107.701</td>\n", "      <td>107.703</td>\n", "      <td>107.748</td>\n", "      <td>107.653</td>\n", "      <td>160</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-06-01 21:00:00</th>\n", "      <td>107.703</td>\n", "      <td>107.693</td>\n", "      <td>107.863</td>\n", "      <td>107.385</td>\n", "      <td>154702</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     askopen  askclose  askhigh   asklow  tickqty\n", "date                                                             \n", "2020-05-21 21:00:00  107.589   107.613  107.854  107.528   189628\n", "2020-05-22 21:00:00  107.613   107.661  107.770  107.329   168381\n", "2020-05-24 21:00:00  107.661   107.660  107.667  107.635        6\n", "2020-05-25 21:00:00  107.660   107.753  107.789  107.576   112616\n", "2020-05-26 21:00:00  107.753   107.566  107.930  107.407   191402\n", "2020-05-27 21:00:00  107.566   107.740  107.955  107.373   274980\n", "2020-05-28 21:00:00  107.740   107.676  107.909  107.576   251491\n", "2020-05-29 21:00:00  107.676   107.843  107.903  107.086   344564\n", "2020-05-31 21:00:00  107.701   107.703  107.748  107.653      160\n", "2020-06-01 21:00:00  107.703   107.693  107.863  107.385   154702"]}, "execution_count": 34, "metadata": {}, "output_type": "execute_result"}], "source": ["candles[candles.columns[4:]]"]}, {"cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": ["start = dt.datetime(2019, 1, 1)\n", "end = dt.datetime(2020, 6, 1)"]}, {"cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": ["candles = api.get_candles('EUR/GBP', period='D1',\n", "                          start=start, stop=end)"]}, {"cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 438 entries, 2019-01-02 22:00:00 to 2020-06-01 21:00:00\n", "Data columns (total 9 columns):\n", " #   Column    Non-Null Count  Dtype  \n", "---  ------    --------------  -----  \n", " 0   bidopen   438 non-null    float64\n", " 1   bidclose  438 non-null    float64\n", " 2   bidhigh   438 non-null    float64\n", " 3   bidlow    438 non-null    float64\n", " 4   askopen   438 non-null    float64\n", " 5   askclose  438 non-null    float64\n", " 6   askhigh   438 non-null    float64\n", " 7   asklow    438 non-null    float64\n", " 8   tickqty   438 non-null    int64  \n", "dtypes: float64(8), int64(1)\n", "memory usage: 34.2 KB\n"]}], "source": ["candles.info()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The parameter `period` must be one of `m1, m5, m15, m30, H1, H2, H3, H4, H6, H8, D1, W1` or `M1`."]}, {"cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": ["candles = api.get_candles('EUR/USD', period='m1', number=250)"]}, {"cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["candles['askclose'].plot(figsize=(10, 6))\n", "plt.savefig('../../images/ch09/fxcm_plot_03.png');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Streaming Data"]}, {"cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": ["def output(data, dataframe):\n", "    print('%3d | %s | %s | %6.5f, %6.5f' \n", "          % (len(dataframe), data['Symbol'],\n", "             pd.to_datetime(int(data['Updated']), unit='ms'), \n", "             data['Rates'][0], data['Rates'][1]))"]}, {"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["  2 | EUR/USD | 2020-06-01 12:49:52.904000 | 1.11049, 1.11062\n", "  3 | EUR/USD | 2020-06-01 12:49:54.481000 | 1.11055, 1.11066\n", "  4 | EUR/USD | 2020-06-01 12:49:54.592000 | 1.11055, 1.11068\n", "  5 | EUR/USD | 2020-06-01 12:49:57.317000 | 1.11057, 1.11068\n", "  6 | EUR/USD | 2020-06-01 12:49:58.104000 | 1.11056, 1.11068\n", "  7 | EUR/USD | 2020-06-01 12:49:58.827000 | 1.11055, 1.11068\n"]}], "source": ["api.subscribe_market_data('EUR/USD', (output,))"]}, {"cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [{"data": {"text/plain": ["Bid     1.11055\n", "Ask     1.11068\n", "High    1.11548\n", "Low     1.10999\n", "Name: 2020-06-01 12:49:58.827000, dtype: float64"]}, "execution_count": 42, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_last_price('EUR/USD')"]}, {"cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": ["api.unsubscribe_market_data('EUR/USD')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Placing Orders"]}, {"cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Empty DataFrame\n", "Columns: []\n", "Index: []"]}, "execution_count": 44, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_open_positions()"]}, {"cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": ["order = api.create_market_buy_order('EUR/USD', 100)"]}, {"cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": ["sel = ['tradeId', 'amountK', 'currency',\n", "       'grossPL', 'isBuy']"]}, {"cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>tradeId</th>\n", "      <th>amountK</th>\n", "      <th>currency</th>\n", "      <th>grossPL</th>\n", "      <th>isBuy</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>167805715</td>\n", "      <td>100</td>\n", "      <td>EUR/USD</td>\n", "      <td>-11.70591</td>\n", "      <td>True</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     tradeId  amountK currency   grossPL  isBuy\n", "0  167805715      100  EUR/USD -11.70591   True"]}, "execution_count": 47, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_open_positions()[sel]"]}, {"cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": ["order = api.create_market_buy_order('EUR/GBP', 50)"]}, {"cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>tradeId</th>\n", "      <th>amountK</th>\n", "      <th>currency</th>\n", "      <th>grossPL</th>\n", "      <th>isBuy</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>167805715</td>\n", "      <td>100</td>\n", "      <td>EUR/USD</td>\n", "      <td>-10.80536</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>167805717</td>\n", "      <td>50</td>\n", "      <td>EUR/GBP</td>\n", "      <td>-10.60469</td>\n", "      <td>True</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     tradeId  amountK currency   grossPL  isBuy\n", "0  167805715      100  EUR/USD -10.80536   True\n", "1  167805717       50  EUR/GBP -10.60469   True"]}, "execution_count": 49, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_open_positions()[sel]"]}, {"cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": ["order = api.create_market_sell_order('EUR/USD', 25)"]}, {"cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": ["order = api.create_market_buy_order('EUR/GBP', 50)"]}, {"cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>tradeId</th>\n", "      <th>amountK</th>\n", "      <th>currency</th>\n", "      <th>grossPL</th>\n", "      <th>isBuy</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>167805715</td>\n", "      <td>100</td>\n", "      <td>EUR/USD</td>\n", "      <td>-13.50706</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>167805717</td>\n", "      <td>50</td>\n", "      <td>EUR/GBP</td>\n", "      <td>-7.25536</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>167805718</td>\n", "      <td>25</td>\n", "      <td>EUR/USD</td>\n", "      <td>-1.12547</td>\n", "      <td>False</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>167805719</td>\n", "      <td>50</td>\n", "      <td>EUR/GBP</td>\n", "      <td>-11.72019</td>\n", "      <td>True</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     tradeId  amountK currency   grossPL  isBuy\n", "0  167805715      100  EUR/USD -13.50706   True\n", "1  167805717       50  EUR/GBP  -7.25536   True\n", "2  167805718       25  EUR/USD  -1.12547  False\n", "3  167805719       50  EUR/GBP -11.72019   True"]}, "execution_count": 52, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_open_positions()[sel]"]}, {"cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": ["api.close_all_for_symbol('EUR/GBP')"]}, {"cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>tradeId</th>\n", "      <th>amountK</th>\n", "      <th>currency</th>\n", "      <th>grossPL</th>\n", "      <th>isBuy</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>167805715</td>\n", "      <td>100</td>\n", "      <td>EUR/USD</td>\n", "      <td>-17.10956</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>167805718</td>\n", "      <td>25</td>\n", "      <td>EUR/USD</td>\n", "      <td>-0.22510</td>\n", "      <td>False</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     tradeId  amountK currency   grossPL  isBuy\n", "0  167805715      100  EUR/USD -17.10956   True\n", "1  167805718       25  EUR/USD  -0.22510  False"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_open_positions()[sel]"]}, {"cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": ["api.close_all()"]}, {"cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Empty DataFrame\n", "Columns: []\n", "Index: []"]}, "execution_count": 56, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_open_positions()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Account Information"]}, {"cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [{"data": {"text/plain": ["1224934"]}, "execution_count": 57, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_default_account()"]}, {"cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>0</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>t</th>\n", "      <td>6</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ratePrecision</th>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>accountId</th>\n", "      <td>1224934</td>\n", "    </tr>\n", "    <tr>\n", "      <th>balance</th>\n", "      <td>49924.1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>usdMr</th>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mc</th>\n", "      <td>N</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mcDate</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>accountName</th>\n", "      <td>01224934</td>\n", "    </tr>\n", "    <tr>\n", "      <th>usdMr3</th>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>hedging</th>\n", "      <td>Y</td>\n", "    </tr>\n", "    <tr>\n", "      <th>usableMargin3</th>\n", "      <td>49924.1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>usableMarginPerc</th>\n", "      <td>100</td>\n", "    </tr>\n", "    <tr>\n", "      <th>usableMargin3Perc</th>\n", "      <td>100</td>\n", "    </tr>\n", "    <tr>\n", "      <th>equity</th>\n", "      <td>49924.1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>usableMargin</th>\n", "      <td>49924.1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>bus</th>\n", "      <td>1000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>dayPL</th>\n", "      <td>-75.95</td>\n", "    </tr>\n", "    <tr>\n", "      <th>grossPL</th>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                          0\n", "t                         6\n", "ratePrecision             0\n", "accountId           1224934\n", "balance             49924.1\n", "usdMr                     0\n", "mc                        N\n", "mcDate                     \n", "accountName        01224934\n", "usdMr3                    0\n", "hedging                   Y\n", "usableMargin3       49924.1\n", "usableMarginPerc        100\n", "usableMargin3Perc       100\n", "equity              49924.1\n", "usableMargin        49924.1\n", "bus                    1000\n", "dayPL                -75.95\n", "grossPL                   0"]}, "execution_count": 58, "metadata": {}, "output_type": "execute_result"}], "source": ["api.get_accounts().T"]}, {"cell_type": "markdown", "metadata": {}, "source": ["<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n", "\n", "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6"}}, "nbformat": 4, "nbformat_minor": 4}